A decade of HIV care in rural Tanzania: Trends in clinical outcomes and impact of clinic optimisation in an open, prospective cohort

A decade of HIV care in rural Tanzania: Trends

in clinical outcomes and impact of clinic

optimisation in an open, prospective cohort

1 Department of Epidemiology & Public Health, Swiss Tropical & Public Health Institute, Basel, Switzerland, 2 University of Basel, Basel, Switzerland, 3 Ifakara Health Institute, Ifakara, Tanzania, 4 ISGlobal, Barcelona

Centre for International Health Research (CRESIB), Hospital Clinic-Universitat de Barcelona, Barcelona, Spain, 5 Bern University Hospital, Bern, Switzerland, 6 University of Bern, Bern, Switzerland, 7 Department of Infectious Diseases and Hospital Epidemiology, Department of Clinical Research, University Hospital Basel, Basel, Switzerland

¶ Membership of the KIULARCO Study Group is provided in the Acknowledgments. *tracy.glass@unibas.ch

Abstract

Objectives

Our objectives were to describe trends in enrolment and clinical outcomes in the open, pro-spective Kilombero and Ulanga Antiretroviral Cohort (KIULARCO) in the Morogoro region of southern Tanzania, and identify strengths and areas for improvement in the care of HIV-pos-itive individuals in rural Tanzania.

Methods

We included adults (15 years) and children (<15 years) enrolled in the cohort in 2005– 2014. The cohort underwent significant changes from autumn 2012 to optimise care. We evaluated mortality and loss to follow-up (LTFU) using competing risks methods, ART usage, opportunistic infections (OI), co-infections and laboratory abnormalities.

Results

Overall, 7010 adults and 680 children were enrolled; enrolment peaked in 2008 but has increased steadily since 2011. Among adults (65% female; median age 37 [interquartile range 31–45] years), the proportion referred from hospital wards quadrupled in 2013–14 versus earlier years. 653 (9%) adults died and 2648 (38%) were LTFU; the five-year cumu-lative probabilities of death and LTFU were 10.3% and 44.0%, respectively. Among children, 69 (10%) died and 225 (33%) were LTFU. The corresponding five-year probabilities were 12.1% and 39.6%. Adult ART use (regardless of eligibility) increased from 5% in 2005 to 89% in 2014 (similarly among children), with 9% on second-line therapy in 2014 (17% of children). OI diagnoses increased over time; tuberculosis prevalence at enrolment a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS

Citation: Vanobberghen F, Letang E, Gamell A, Mnzava DK, Faini D, Luwanda LB, et al. (2017) A decade of HIV care in rural Tanzania: Trends in clinical outcomes and impact of clinic optimisation in an open, prospective cohort. PLoS ONE 12(7): e0180983.https://doi.org/10.1371/journal. pone.0180983

Editor: Philip Anglewicz, Tulane University School of Public Health and Tropical Medicine, UNITED STATES

Received: May 9, 2016 Accepted: June 24, 2017 Published: July 18, 2017

Copyright:© 2017 Vanobberghen et al. This is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability Statement: Data are available from the KIULARCO Scientific Committee, via the Project Head (https://www.swisstph.ch/en/ projects/tanzania-chronic-disease-clinic-ifakara/). KIULARCO has procedures in place for data sharing to protect the confidentiality of the participants and ensure the scientific integrity of the cohort. All requests for data must be submitted to the Scientific Committee with a Letter of Intent;

quadrupled from 6% in 2011 to 26% in 2014. The proportion of newly-enrolled participants assessed for laboratory abnormalities peaked at nearly 100% in 2014 (from a minimum of 24%), yet abnormality prevalences remained fairly constant.

Conclusions

In this cohort, ART usage improved dramatically and is approaching targets of 90%. Improved screening led to increases in detection of OIs and laboratory abnormalities, sug-gesting that a large number of these co-morbidities previously went undetected and untreated. Further work will address the high LTFU rates and implications for mortality esti-mates, and the management and outcomes of co-morbidities.

Introduction

The burden of the HIV epidemic in Tanzania is large, with an estimated 1.5 million adults and children living with HIV in 2012 [1]. The national HIV/AIDS programme was launched in 2004 in response to the rising epidemic [2]. In 2004, the Chronic Diseases Clinic of Ifakara (CDCI) was established within the St Francis Referral Hospital (SFRH), which serves over 600,000 people in the Kilombero and Ulanga districts of the Morogoro region in southern Tanzania [3]. The CDCI was the first rural HIV care and treatment clinic accredited to provide HIV services through the National AIDS Control programme (NACP). Within the framework of the CDCI, a prospective, open cohort was initiated in 2005 by the Ifakara Health Institute, in collaboration with the Swiss Tropical & Public Health Institute and the University Hospital Basel. The Kilombero and Ulanga Antiretroviral Cohort (KIULARCO), with systematic data collection on clinical parameters including antiretroviral therapy (ART) use, co-morbidities and clinical outcomes such as opportunistic infections (OIs) and mortality, plus a plasma bio-bank spanning ten years, has become a unique research platform.

Since autumn 2012, significant changes have been made at the CDCI targeting education of the clinical staff, optimisation of the patient circuit, implementation of routine diagnostic tests, better integration of HIV services with other units and SFRH wards, and an extensive overhaul of data collection along with the launch of comprehensive electronic medical records. Early evidence suggests that implementation of this broad bundle of services has improved the running of the clinic and enhanced care, such as through earlier and better detection of tuber-culosis co-infection [4]. Despite recommendations of increasing CD4 thresholds for ART initi-ation, most people living with HIV in Tanzania still present late in the course of HIV disease. Late presentation is associated with higher prevalence of OIs and non-AIDS co-morbidities, incidence of immune reconstitution inflammatory syndrome (IRIS) after ART initiation, rate of hospitalisations, use of healthcare resources, and mortality [5–7]. Thus, pre-ART screening of OIs and co-morbidities is a key component to improving outcomes in sub-Saharan Africa. Since 2012, a systematic screening of tuberculosis, cryptococcosis, hepatitis B, syphilis, and renal and liver disease is performed at the CDCI prior to ART initiation.

The aim of this paper is to present the trends over the ten-year data collection period in enrolment and retention in HIV care, mortality, ART usage, and rates of detection of OIs, co-infections and laboratory abnormalities. Our results give a comprehensive overview of the cohort, contribute to the currently-limited knowledge of comorbidities among HIV-positive persons in Tanzania, and identify strengths and areas for improvement in the care of HIV-pos-itive individuals in rural Tanzania.

this applies to persons both internal and external to the cohort.

Funding: This work was supported by the Swiss Tropical & Public Health Institute and development funds from the Canton of Basel-Stadt, Switzerland. The Kilombero and Ulanga Antiretroviral Cohort and the Chronic Diseases Clinic Ifakara receive financial support from the Government of the Canton of Basel, Switzerland, the Swiss Tropical & Public Health Institute, the Ifakara Health Institute, the Government of Tanzania, and the United States Agency for International Development through TUNAJALI-Deloitte. Cryptococcal antigen lateral flow assays are donated by IMMY, Norman, Oklahoma. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Cryptococcal antigen lateral flow assays are donated by IMMY, Norman, Oklahoma; this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Methods

Study design and population

The CDCI, through the NACP, offers free HIV care and treatment services. Attendees are those identified as HIV-positive following presentation for voluntary counselling and testing (VCT) or through provider-initiated counselling and testing (PITC) while admitted to the SFRH wards, or those who have transferred in from another clinic; all are invited to participate in KIULARCO. For this analysis of prospectively-collected cohort data, we included all patients enrolled from 2005 to 2014, except patients who refused consent; HIV-negative patients; transit patients (enrolled in other clinics but who attended CDCI temporarily, usually for a drug refill while travelling); and patients with missing enrolment or birth dates. We included longitudinal data through to 31 December 2014.

Clinic procedures and data capture

Since autumn 2012, a number of changes to HIV services were implemented at the CDCI to optimise care. Changes included introduction of PITC in the SFRH wards, and screening of OIS and non-AIDS co-morbidities, including improvements in tuberculosis detection and care with integration of the tuberculosis clinic within the CDCI [4,8]. Routine enrolment screening services now include hepatitis B, syphilis and, for patients with CD4 count <150 cells/mm3, cryptococcal antigenaemia using cryptococcal antigen (CRAG) lateral flow assay (IMMY, Norman, Oklahoma) [9]. A One Stop Clinic was created to provide dedicated care for HIV-positive pregnant women, children and their families [10] and HIV pro-viral DNA PCR was implemented for early infant diagnosis.

Since May 2013, the CDCI is paperless; new comprehensive data capture forms were trans-lated into an electronic medical record system where patient data are entered directly during their visit. At registration, we capture sociodemographic data and medical history. During clinical visits, we record vital signs and laboratory results, perform a structured physical exami-nation to determine working diagnoses (captured through ICD-10 codes [11]), and assess treatment adherence, failure and toxicity, and IRIS. ART, cotrimoxazole prophylaxis, isoniazid preventive treatment and fluconazole prophylaxis for CRAG-positive patients are prescribed electronically and dispensed from the clinic pharmacy to the patient or treatment supporter. Visits are scheduled at least every 3 months for those on ART and every 6 months for those not yet on ART.

Diagnosis of OIs, IRIS, co-infections and laboratory abnormalities

among adults

For this study, tuberculosis diagnosis was defined as positive microscopy with acid-fast bacilli, positive Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA) in sputum or other extra pul-monary sample, chest radiograph suggestive of tuberculosis plus at least one symptom, taking anti-tuberculosis medication, or physician diagnosis. Cryptococcosis, Kaposi’s sarcoma, Pneu-mocystis jirovecii pneumonia (PCP), toxoplasmosis, non-Hodgkin’s lymphoma and bacterial

pneumonia were physician-diagnosed, with cryptococcosis confirmed by CRAG-positive in plasma and cryptocococcal meningitis in cerebrospinal fluid. IRIS, as diagnosed by the clini-cian, was categorised as suspected, probable or definite adapting commonly-accepted case def-initions [12–15]. Syphilis was diagnosed by Venereal Disease Research Laboratory test and hepatitis B by one surface antigen test. Severe anaemia was defined as haemoglobin <8 g/dL [16], liver impairment as alanine transaminase >2_{56 U/L (where 56 U/L is the upper limit of}

normal) [17], and renal impairment as creatinine clearance <90 ml/min/1.73m2, calculated using the Chronic Kidney Disease Epidemiology Collaboration equation [18].

Statistical methods

Analyses are presented for adults (age 15 years at enrolment) and children (<15 years), by enrolment year. After assessing data distributions, similar time periods were grouped together, which also corresponded with changes in cohort leadership. We present participant character-istics at enrolment using appropriate summary statcharacter-istics. We evaluated the number and pro-portion of individuals on ART according to first (non-nucleoside reverse transcriptase inhibitor-based) or second line (boosted protease inhibitor-based) therapy, and we describe ART initiations among adults. We assessed mortality and lost to follow-up (LTFU), defined as

>60 days passed since their next planned visit [19]. Participants who had not died, were not LTFU and had not transferred out were considered to be in follow-up to 31 December 2014. We estimated time to death or LTFU using competing risks methods [20], with results pre-sented as cumulative incidences and 95% confidence intervals (CI).

Among adults, we evaluated tuberculosis prevalence at enrolment (diagnosed within the first month of enrolment, which may have been previously diagnosed outside of the clinic) and incidence amongst those not positive at enrolment. We evaluated other OI diagnoses fol-lowing a CD4 count <200 cells/mm3. We assessed the testing and diagnosis of laboratory abnormalities within one month of enrolment.

Analyses were performed using Stata version 13 (StataCorp. 2013.Stata Statistical Software: Release 13. College Station, TX: StataCorp LP).

Ethical statement

The Ifakara Health Institute Institutional Review Board and the National Health Research Ethics Review Committee of the National Institute for Medical Research of Tanzania provide ethical approval for KIULARCO, including for sample collection, cryopreservation and retro-spective analysis. Written informed consent is sought from all participants at registration at the CDCI; for children and adolescents aged <18 years, informed consent is sought from their caregivers. Those who refused consent were excluded from our analyses. Individualised clini-cal consultations take place in separate rooms at the CDCI, to ensure privacy. Patient-friendly schedules are followed, and each clinician has an assigned pool of patients to improve patient satisfaction and compliance. All data analysed for this study, including laboratory results from samples, were captured under routine care at the CDCI; data are stored on a secure server and were de-identified before analysis.

Results

Cohort enrolment

Of 10,191 individuals, we excluded 1569 (15%) who refused participation, 15 (<1%) subse-quently identified as HIV-negative, 779 (8%) transit patients, 121 (1%) who were missing enrolment date and 17 (<1%) who were missing date of birth. Therefore, we included 7010 adults and 680 children in the analysis (S1 Fig). At least 400 participants were enrolled every year since 2005, with a peak in 2008 and a further steady increase over recent years (Fig 1A).

Participant characteristics at enrolment

The adult sociodemographic characteristics remained fairly stable over time (Table 1), with 42% residing in Ifakara, where the CDCI is situated. Patients mostly came to the CDCI via

Fig 1. Enrolment and ART usage over time. A) Enrolment. B) ART usage among adults. C) ART usage

among children. Adults15 years; children<15 years. Participants on ART at any point in a given year were classified as on ART. Denominator is patients with any clinical or drug visit in a given year. Percentages may not add up to 100% due to rounding. Percentages<1% are not shown.

Table 1. Demographic and clinical characteristics of adults (15 years) at enrolment (n = 7010). Year of enrolment 2005–07 2008–09 2010–12 2013–14 Total Number enrolled 1827 (100%) 2351 (100%) 1611 (100%) 1221 (100%) 7010 (100%) Ward Ifakara 632 (35.7%) 880 (37.8%) 901 (56.3%) 498 (40.9%) 2911 (42.1%) Mngeta 139 (7.9%) 272 (11.7%) 74 (4.6%) 208 (17.1%) 693 (10.0%) Mang’ula 118 (6.7%) 231 (9.9%) 58 (3.6%) 69 (5.7%) 476 (6.9%) Mlimba 80 (4.5%) 213 (9.1%) 9 (0.6%) 46 (3.8%) 348 (5.0%) Othera _{800 (45.2%) 735 (31.5%) 557 (34.8%) 397 (32.6%) 2489 (36.0%)}

Distance from home to clinic, kmb _{2 (1–70) 25 (1–81) 1 (1–20) 5 (1–51) 1 (1–51)}

Patient referred fromc

Voluntary counselling and testing or self-referral 956 (52.3%) 1925 (81.9%) 1223 (75.9%) 863 (70.7%) 4967 (70.9%) Reproductive and child health clinic 69 (3.8%) 113 (4.8%) 136 (8.4%) 53 (4.3%) 371 (5.3%)

Out-patient department 254 (14.6%) 45 (2.0%) 47 (3.1%) 15 (1.2%) 361 (5.4%)

In-patient care hospitalisation (PITC) 24 (1.6%) 75 (3.4%) 47 (3.1%) 125 (10.2%) 271 (4.2%)

Transfer in 0 (0.0%) 31 (1.3%) 44 (2.7%) 213 (17.4%) 288 (4.1%)

Tuberculosis or DOTS 86 (4.7%) 50 (2.1%) 47 (2.9%) 3 (0.2%) 186 (2.7%)

Other 105 (5.7%) 40 (1.7%) 56 (3.5%) 27 (2.2%) 228 (3.3%)

Sex, female 1194 (65.4%) 1509 (64.2%) 1038 (64.5%) 816 (66.9%) 4557 (65.0%)

Marital status, married/cohabiting 800 (48.4%) 1197 (51.6%) 805 (50.7%) 758 (62.2%) 3560 (52.5%)

Education, none/primary school only 497 (94.7%) 766 (94.9%) 805 (93.4%) 1151 (94.3%) 3219 (94.3%)

Age, years 37 (31–44) 37 (31–45) 37 (31–45) 37 (31–45) 37 (31–45)

Pregnantd _{26 (3.7%) 62 (6.6%) 88 (10.0%) 76 (10.3%) 252 (7.7%)}

Partner status, HIV-positivee 118 (69.4%) 313 (66.2%) 239 (62.6%) 239 (71.3%) 909 (66.8%)

Disclosure of HIV status toc

Partner 60 (3.4%) 152 (6.6%) 354 (24.0%) 358 (38.5%) 924 (14.2%)

Relative 41 (2.3%) 183 (7.9%) 475 (32.1%) 599 (64.5%) 1298 (20.0%)

Time from HIV diagnosis to enrolment, months 1.6 (0.5–7.4) 0.5 (0.1–1.3) 0.3 (0.1–0.5) 0.1 (0.0–0.5) 0.4 (0.1–2.0)

BMI, kg/m2 Underweight (<18.5) 229 (29.6%) 216 (27.9%) 294 (20.6%) 262 (22.6%) 1001 (24.2%) Normal weight (18.5-<25) 451 (58.3%) 458 (59.1%) 866 (60.7%) 703 (60.7%) 2478 (59.9%) Overweight (25-<30) 75 (9.7%) 83 (10.7%) 212 (14.9%) 143 (12.3%) 513 (12.4%) Obese (30) 18 (2.3%) 18 (2.3%) 55 (3.9%) 51 (4.4%) 142 (3.4%) WHO stage I 596 (38.1%) 779 (33.5%) 653 (41.7%) 433 (39.9%) 2461 (37.6%) II 246 (15.7%) 666 (28.7%) 347 (22.1%) 160 (14.8%) 1419 (21.7%) III 448 (28.6%) 603 (25.9%) 431 (27.5%) 296 (27.3%) 1778 (27.2%) IV 275 (17.6%) 276 (11.9%) 136 (8.7%) 195 (18.0%) 882 (13.5%) CD4 count, cells/mm3 <50 85 (15.7%) 216 (14.1%) 117 (14.0%) 179 (17.4%) 597 (15.1%) 50–99 81 (14.9%) 180 (11.7%) 98 (11.7%) 123 (12.0%) 482 (12.2%) 100–199 119 (21.9%) 319 (20.8%) 155 (18.6%) 183 (17.8%) 776 (19.7%) 200–349 117 (21.5%) 322 (21.0%) 188 (22.5%) 219 (21.3%) 846 (21.5%) 350–499 55 (10.1%) 222 (14.5%) 129 (15.4%) 141 (13.7%) 547 (13.9%) (Continued )

VCT or self-referral, but in-patient referrals (diagnosed through PITC) and transfers-in rose significantly in 2013–14 while out-patient referrals dropped after 2005–07. The majority of participants were female (65%), were married/cohabiting (53%) and only had at most primary school education (94%). The median (interquartile range [IQR]) age was 37 (31–45) years. Reported disclosure of HIV status to partners and relatives increased respectively from 3% and 2% in 2005–07 to 39% and 65% in 2013–14. Over the same time period, the median time from reported HIV diagnosis to enrolment decreased from 1.6 months (IQR 0.5–7.4) to 0.1 months (0.0–0.4). Trends toward lower WHO stage at enrolment reversed in 2013–14, approaching 2005–07 levels (41% WHO stage III/IV overall), while the proportion of patients with CD4 count <350 cells/mm3remained fairly stable after 2005–07 (69% overall).

Among children, the median age at enrolment decreased, from 6 (IQR 3–9) years in 2005– 07 to 4 years (1–8) in 2013–14, as did the time from HIV diagnosis to enrolment (from 1.0 (0.2–5.4) to 0.1 (0.0–0.5) months;Table 2). Over the same time periods, the percentage of chil-dren with WHO stage III/IV increased (from 47% to 77%).

ART usage

Overall, 4989 (71%) adults and 513 (75%) children ever received ART under the care of the CDCI, of whom 484 (10%) and 41 (8%), respectively, had prior ART exposure. Of adults attending the CDCI in any given year, the percentage of patients on ART (regardless of eligibil-ity) rapidly increased from 5% in 2005 to at least 80% since 2010 (Fig 1B), with similar patterns among children (Fig 1C). The proportion of adults on second line therapy increased from none pre-2008 to 9% in 2014, while the proportion of children on second line therapy increased rapidly from 1% pre-2012 to 17% in 2014. Among 361 adults not on ART in 2014, 191 (53%) were not yet eligible for therapy. Of the remaining 170 adults, 132 (37%) had been under follow-up for <90 days, 1 (<1%) did not have eligibility information captured, and 37 (22%) had been under follow-up for at least 90 days and were eligible for ART but had not yet started.

ART initiations among adults

Overall, 4502 adults initiated ART (excluding women starting for prevention of mother-to-child HIV transmission) for the first time at the CDCI (Table 3). In 2005–2007, 70% of patients

Table 1. (Continued)

Year of enrolment

2005–07 2008–09 2010–12 2013–14 Total

500 86 (15.8%) 277 (18.0%) 148 (17.7%) 183 (17.8%) 694 (17.6%)

Results are median (interquartile range) for continuous variables and number (column % of non-missing data) for categorical variables. Variables with>10% missing data: distance from home to clinic (17% missing, since not all wards mapped), education (51% missing, mainly from earlier years), BMI (41% missing, mainly due to missing height), CD4 count (44% missing, mainly from earlier years). CD4 count and WHO stage at enrolment were defined as the measurement closest to the date of enrolment, within -24 to +4 weeks (preference for earlier). BMI, body mass index; DOTS, directly observed treatment, short course; PITC, provider-initiated testing and counselling.

a

Estimated based on ward of residence.

b_{Wards with<10% of participants each have been combined.} c_{Participants may respond yes to more than one category.} d

Percentages are of women with non-missing data (missing for 28% women).

e_{Percentages are of those with results (excluding 81% not applicable/missing).}

Table 2. Demographic and clinical characteristics of children (<15 years) at enrolment (n = 680). Year of enrolment 2005–07 2008–09 2010–12 2013–14 Total Number enrolled 166 (100%) 244 (100%) 150 (100%) 120 (100%) 680 (100%) Sex, female 72 (43.4%) 115 (47.1%) 74 (49.3%) 56 (46.7%) 317 (46.6%) Age, years 6 (3–9) 4 (2–8) 5 (2–9) 4 (1–8) 5 (2–8) Age<5 years 69 (41.6%) 141 (57.8%) 79 (52.7%) 68 (56.7%) 357 (52.5%)

Time from HIV diagnosis to enrolment, months 1.0 (0.2–5.4) 0.3 (0.0–1.3) 0.2 (0.1–1.2) 0.1 (0.0–0.5) 0.3 (0.0–1.6)

WHO stage I 49 (34.0%) 84 (35.9%) 47 (32.2%) 20 (17.2%) 200 (31.3%) II 28 (19.4%) 71 (30.3%) 28 (19.2%) 7 (6.0%) 134 (20.9%) III 39 (27.1%) 66 (28.2%) 54 (37.0%) 50 (43.1%) 209 (32.7%) IV 28 (19.4%) 13 (5.6%) 17 (11.6%) 39 (33.6%) 97 (15.2%) CD4<25% 55 (87.3%) 113 (72.4%) 49 (62.8%) 82 (75.9%) 299 (73.8%)

Results are median (interquartile range) for continuous variables and number (column % of non-missing data) for categorical variables. Variables with>10% missing data: CD4% (40% missing, mainly from earlier years). CD4 count and WHO stage at enrolment were defined as the measurement closest to the date of enrolment, within -24 to +4 weeks (preference for earlier).

https://doi.org/10.1371/journal.pone.0180983.t002

Table 3. Characteristics of adults (15 years) at ART initiation.

Year of ART initiation

2005–07 2008–09 2010–12 2013–14 Total

Number initiated ART 679 (100%) 1547 (100%) 1358 (100%) 918 (100%) 4502 (100%)

Time from enrolment to ART initiation, monthsa 4.7 (0.9–12.6) 1.0 (0.2–4.1) 0.5 (0.1–8.1) 0.5 (0.2–2.5) 0.9 (0.2–6.2)

ART regimen started

d4T+3TC+NVP 474 (69.8%) 915 (59.1%) 160 (11.8%) 0 (0.0%) 1549 (34.4%) d4T+3TC+EFV 22 (3.2%) 45 (2.9%) 9 (0.7%) 0 (0.0%) 76 (1.7%) AZT+3TC+NVP 7 (1.0%) 22 (1.4%) 125 (9.2%) 53 (5.8%) 207 (4.6%) AZT+3TC+EFV 172 (25.3%) 503 (32.5%) 490 (36.1%) 118 (12.9%) 1283 (28.5%) TDF+FTC+EFV 0 (0.0%) 0 (0.0%) 239 (17.6%) 352 (38.3%) 591 (13.1%) TDF+3TC+EFV 0 (0.0%) 0 (0.0%) 0 (0.0%) 382 (41.6%) 382 (8.5%)

Other first line 0 (0.0%) 0 (0.0%) 3 (0.2%) 3 (0.3%) 6 (0.1%)

Second line (boosted PI-based) 2 (0.3%) 0 (0.0%) 7 (0.5%) 10 (1.1%) 19 (0.4%)

Otherb _{2 (0.3%) 62 (4.0%) 325 (23.9%) 0 (0.0%) 389 (8.6%)}

CD4 count at initiation, cells/mm3

<100 134 (26.7%) 412 (35.8%) 208 (26.4%) 249 (31.1%) 1003 (31.0%)

100–199 162 (32.3%) 355 (30.9%) 187 (23.7%) 166 (20.8%) 870 (26.9%)

200–349 125 (25.0%) 262 (22.8%) 262 (33.2%) 236 (29.5%) 885 (27.3%)

350–499 45 (9.0%) 66 (5.7%) 74 (9.4%) 91 (11.4%) 276 (8.5%)

500 35 (7.0%) 55 (4.8%) 58 (7.4%) 58 (7.2%) 206 (6.4%)

Results are median (interquartile range) for continuous variables and number (column % of non-missing data) for categorical variables. Variables with>10% missing data: CD4 count (26%, 26%, 42% and 16% missing in 2005–07, 2008–09, 2010–12, 2013–14, respectively). CD4 count at ART initiation was defined as the measurement closest to the date of initiation, within-24 to +4 weeks (preference for earlier). d4T, stavudine; 3TC, lamivudine; NVP, nevirapine; EFV, efavirenz; AZT, zidovudine; TDF, tenofovir disoproxil fumarate; FTC, emtricitabine.

a_{This table is restricted to participants who were observed to initiate ART, and therefore does not take into account participants who were not (yet) observed}

to initiate ART.

b_{Regimen unknown therefore not possible to classify as first or second line.}

initiated on stavudine, lamivudine and nevirapine. Over time, efavirenz replaced nevirapine, the backbone of tenofovir disoproxil fumarate with emtricitabine or lamivudine became more common (80% of initiations in 2013–14), and stavudine was phased out, in line with national guidelines [21]. There were some albeit small increases over time in the proportion of partici-pants initiating ART with CD4 count 350 cells/mm3, at 16%, 11%, 17% and 19% in 2005–07, 2008–09, 2010–12 and 2013–14, respectively.

Mortality and loss to follow-up

Over approximately 18,000 person-years at risk, 653 (9%) adults died and 2648 (38%) were LTFU (S1 Fig). By five years after enrolment, the cumulative probability of death was 10.3% (95% CI 9.5–11.1) and LTFU was 44.0% (42.7–45.4). By year of registration, the probabilities of reported death at 12 months after enrolment were 11.6% (10.2–13.1), 6.3% (5.4–7.4), 3.0% (2.2–3.9) and 5.2% (3.9–6.8) among those registered in 2005–07, 2008–09, 2010–12 and 2013– 14, respectively (Fig 2A). The corresponding values for LTFU were 22.0% (20.1–24.0), 26.5% (24.7–28.4), 26.7% (24.5–28.9) and 21.9% (19.0–24.9;Fig 2B).

Over approximately 1900 person-years at risk, 69 (10%) children died and 225 (33%) were LTFU (S1 Fig). By five years after enrolment, the cumulative probability of death was 12.1% (9.5–15.1) and LTFU was 39.6% (35.2–43.9). Over the same time periods as above, the proba-bilities of reported death by 12 months after enrolment were 8.2% (4.6–13.1), 8.4% (5.3–12.3), 1.4% (0.3–4.5) and 9.4% (4.7–15.9), respectively (Fig 2C). The corresponding values for LTFU were 19.0% (13.3–25.5), 23.3% (18.1–28.8), 19.1% (13.1–26.0) and 12.3% (5.5–22.1;Fig 2D).

Fig 2. Incidence of death and loss to follow up (LTFU). A) Adults: Death by enrolment year. B) Adults:

LTFU by enrolment year. C) Children: Death by enrolment year. D) Children: LTFU by enrolment year. Adults 15 years; children<15 years. Analysis by competing risks. Participants were considered LTFU if more than 60 days had passed since their next planned visit (based on next appointment date, or otherwise assumed to be every 3 months for those on ART and every 6 months for those not yet on ART, not including those known to have died or transferred out). Only the first 12 months since enrolment are shown (since little further follow-up was available for those enrolled in 2013–14), and the y-axes are truncated at 0.5.

Fig 3. Tuberculosis and laboratory abnormalities at enrolment among adults (15 years). A) Diagnosis

of tuberculosis. B) Testing and diagnosis of severe anaemia. C) Testing and diagnosis of liver impairment. D) Testing and diagnosis of renal impairment. For tuberculosis, values show the percentage of diagnoses within the CDCI (within the first month following enrolment; these OIs may have been previously diagnosed outside of the clinic; with denominator the number of participants who had a clinical visit within the first month following

Among both adults and children, the earliest that one can meet the LTFU definition is two months after the next planned visit (Fig 2B and 2D). The larger increases in LTFU at 8 months among adults enrolled in some of the enrolment years are attributable to those who did not start ART at enrolment and did not return to the clinic for a follow-up visit. Among 6515 adults who were ART-naïve before enrolment, the percentage starting ART within 2 weeks of enrolment increased over time (from 7% in 2005–07 to 41% in 2013–14), and those starting ART within this period were less likely to be LTFU within the first year after enrolment than those not starting ART (20% versus 25%;Χ2test, p<0.001). Across all participants, 902 (12%) had a gap in care of >1 year before re-engaging in care (ie returning for a clinic visit), and 487 (6%) had a gap of >2 years.

Opportunistic infections and IRIS among adults

The prevalence of tuberculosis diagnosis among adults at enrolment peaked at 11% in 2008, declined and then rapidly increased after 2012 to 26% in 2014 (Fig 3A). Among those without tuberculosis at enrolment, the incidence peaked at 8% in 2008 and has remained at 2–4% since then.

Before 2011, diagnoses of disseminated cryptococcosis, Kaposi sarcoma and PCP were often not recorded; among adults with results captured at their first CD4 count <200 cells/ mm3, the annual prevalences were typically <1%. However, in 2014, the prevalences were 5.0%, 3.6% and 5.8%, respectively. Toxoplasmosis, non-Hodgkin’s lymphoma and bacterial pneumonia were not consistently recorded before 2013; the prevalences among adults with first CD4 count <200 cells/mm3in 2014 were 2.2%, 0.5% and 8.9%, respectively.

IRIS detection was low pre-2013 (5 diagnoses per year), and increased to 39 (1.7% of adults on ART without previous IRIS) and 49 (1.9%) diagnoses in 2013 and 2014, respectively. In 2013–14, the classification of diagnoses was 58 suspected, 18 probable and 11 definite.

Co-infections among adults

Few syphilis presumptive diagnoses were made before 2014 (12 per year), but 133 new diag-noses were made in 2014 (16% of adults with a visit and not previously positive). Hepatitis B was not routinely captured before 2013, but as the result of a screening initiative in 2014, 199 (7%) new diagnoses of chronic hepatitis B were made in 2014 [22].

Laboratory abnormalities among adults

The proportion of newly-enrolled participants assessed for laboratory abnormalities peaked in 2014 following implementation of routine screening, approaching 100% coverage (96%, 87% and 97% for anaemia, liver impairment and renal impairment, respectively;Fig 3B–3D). Prev-alences of severe anaemia, liver impairment and renal impairment remained between 11–18%, 1–4% and 6–15%, respectively, despite large increases in the proportion tested.

enrolment in that year). For laboratory tests, no tests were performed in 2005 and few in 2006 therefore these years were omitted. A window of-24 to +4 weeks relative to registration date was allowed, with preference for before. Severe anaemia defined as haemoglobin<8 g/dL [16]. Liver impairment defined as alanine

transaminase>2*56 U/L, ie twice the upper limit of normal [17]. Renal impairment defined as creatinine clearance<90 ml/min/1.73m2_{, calculated using the Chronic Kidney Disease Epidemiology Collaboration}

equation [18].

Discussion

In this comprehensive longitudinal analysis of a prospective cohort spanning 10 years and including nearly 8000 HIV-positive patients, we found that a bundle of measures aiming to improve patient care have led to better linkage to care within the hospital, shortening of time between diagnosis and enrolment, increases in ART usage, improved screening of OIs, and higher testing and detection of laboratory abnormalities.

Until 2011, the CDCI observed enrolment patterns similar to national trends [2], but there-after–in contrast to national trends–enrolment began to steadily increase. We attribute the recent increases to the 2014 implementation of consistent PITC in the medical, paediatric and tuberculosis wards, and the out-patient department of SFRH; average monthly enrolment increased from 45 in 2013 to 67 in 2014. Similar enrolment patterns were observed in children, despite being more difficult to diagnose and link to care [23]. The average monthly enrolment increased from three children per month in April-December 2013 to eight in the same time period in 2014 after the launch in early 2014 of the One Stop Clinic, and implementation of PITC and HIV pro-viral DNA PCR for infant diagnosis. In recent years, we found that patients were linking to care sooner following their HIV diagnosis, in part attributable to measures such as the national VCT campaign in 2007–08 [24]. However, we did not observe corre-sponding improvements in disease stage or CD4 count at enrolment, likely related to the implementation of PITC capturing sicker patients, and suggesting that people are still waiting too long to get tested and presenting late in the course of the disease. Our findings are in line with the national data, where approximately 45% of people with a CD4 count result at enrol-ment had <200 cells/mm3[2].

ART usage increased dramatically over time, despite Tanzanian guidelines not recom-mending universal ART initiation for those with CD4 counts >350 cells/mm3at the time of this study [25] and little evidence of changes in CD4 count distributions over time. Our observed rates of ART usage of 89% among adults and 92% among children in 2014 are sub-stantially higher than the reported national coverage (61% and 26% of eligible adults and chil-dren, respectively, in 2012) [26] and are in line with UNAIDS treatment targets for 2020 [23]. At the CDCI in 2014, eligibility to start ART with respect to CD4 count was evaluated in 94% of patients at enrolment compared to a maximum of 70% in earlier years and 27% nationwide in 2011 [25], likely due to our improved clinic circuit and electronic data capture. This acceler-ated process for evaluating eligibility should lead to reductions in loss to follow up, which was 25% by one year in those who did not start ART shortly after enrolment, suggesting they were linked to but failed to remain in care. In light of the results of the START and TEMPRANO tri-als [27,28], moving to a test-and-treat approach will not only lead to earlier engagement in care, but dramatically improve patient outcomes.

Over time, the absolute numbers of diagnoses of OIs and laboratory abnormalities has increased, most notably in 2013–14. This can be attributed to an increasing number of labora-tory tests being performed–in part related to growth in equipment and expertise, policy changes regarding screening at baseline, and improved medical evaluations, training and data capture. Tuberculosis diagnoses at enrolment quadrupled from pre-2011 to 2014, most likely attributable to improvements in detection rather than underlying changes in prevalence, and indicating that infections may have remained undiagnosed in earlier years [4]. Systematic lab-oratory-based CRAG screening among those with low CD4 count, followed by lumbar punc-ture to detect meningitis, allowed reliable ascertainment of disseminated cryptococcosis and cryptococcal meningitis [29]. Our observed prevalence in 2014 of 5% is consistent with other studies (3–7.1% [30]). While globally the incidence of Kaposi sarcoma is decreasing with increasing ART coverage [31,32], we observed increasing prevalence over time, reaching 3.6%

among patients enrolled in 2014 with low CD4 count, most likely due to improved detection, although the limitations of physician-diagnosis are known [33]. Among the same KIULARCO patients, PCP prevalence was lower than in a recent meta-analysis of studies conducted between 1995 and 2015 (5.8% versus 15.4%) but the authors found substantial heterogeneity by time, clinical setting and diagnostic method [34]. Our prevalence was also lower than a study from 2006 (10.4%), however this was in HIV-infected patients presenting with cough [35]. Further, our estimates should be interpreted as upper limits, since diagnostic tools for the etiologic diagnosis were not in place. Importantly, toxoplasmosis, non-Hodgkin’s lymphoma and bacterial pneumonia were not investigated prior to 2012, and the number of diagnoses of bacterial pneumonia in particular indicates that there was previously a large unrecognised and hence untreated problem. Toxoplasmosis was suspected on the basis of focal palsies in patients with low CD4 count, but rarely confirmed through image techniques such as CT scan, which is not available in Ifakara. Some studies report declining incidence of non-Hodgkin’s lym-phoma with increasing ART coverage [32], but a study from Uganda found increasing inci-dence [31]; we observed low prevalence (<1%), which should be cautiously interpreted due to the lack of histologic confirmation. In addition, bacterial pneumonia was not confirmed with pneumococcal antigen or blood cultures but refers to acute respiratory infections with radio-logic consolidation that responded to antimicrobial therapy.

Measuring haematological, renal and liver parameters can aid in choosing the most appro-priate ART regimen to minimise drug toxicity; tenofovir disoproxil fumarate is associated with renal impairment, zidovudine with haematological abnormalities, and nevirapine and efavirenz with liver toxicity, among others [36–43]. In Tanzania, current guidelines recom-mend haematological and biochemistry renal and liver function tests pre-ART and at ART ini-tiation, and haemoglobin, creatinine and ALT monitoring for those on zidovudine, tenofovir disoproxil fumarate and nevirapine respectively [44]. The associated financial burden is likely to be a contributory factor for the high rates of early attrition in some African settings [45]. In the CDCI, these services are provided at no cost to the patient and coverage of baseline laboratory testing increased significantly over time to reach approximately 90% in 2014. How-ever, the prevalence of laboratory abnormalities did not change substantially. Based on this, we estimate that there were 37, 3 and 22 undetected cases of severe anaemia, liver and renal impairment at enrolment, respectively, in 2012 alone (representing 7%, 1% and 4% of adults enrolled in that year, respectively).

The observed trends in death and LTFU are more difficult to interpret. There were clear dif-ferences in the death rates by year of enrolment. In sub-Saharan Africa, the adult mortality rate in the first year after treatment initiation is estimated to be in the range 8–26% [46], there-fore the observed low rate in our cohort in 2010–12 in particular is implausible and suggests that perhaps a large proportion of deaths were not being captured. One-year LTFU rates in adults remained between 22–27% over time but showed trends toward improvement in 2013– 14. The higher observed death rate in children during the first months under care in 2013–14 can likely be attributed to the higher proportions presenting with WHO stage III/IV, due to improved clinical assessment and higher proportions of children identified through PITC in the hospital wards [10]. Our high LTFU rates are in line with similar cohorts [47] and may partly be explained by silent transfers to other clinics [48]. Regardless, such LTFU presents challenges in obtaining accurate mortality estimates. Time-dependent modelling taking into account LTFU rates over time is the focus of on-going work [49].

The impact of the clinic changes has led to improvements in patient care across a broad range of outcomes, and the benefits are felt by both staff and patients, with a decrease in lost test results files and improved efficiency in running the clinic. However, the implementation of these changes has not been without challenges. The increased ascertainment of HIV through

the consistent implementation of PITC has led to an overburdened clinic with long waiting times and intensified demand for human resources. Further, there is a shortage of skilled and HIV-experienced healthcare workers and we face difficulties retaining them in peripheral rural settings. Moreover, structural system barriers lead to frequent shortages of reagents, anti-retrovirals and other drugs for OIs, thus impairing patients’ outcomes. Finally, developing and maintaining an electronic medical record system in this setting has been hampered by limita-tions in infrastructure, power cuts, and a lack of local technological expertise. Strengths of the cohort are the large number of patients enrolled over the last decade, with the accompanying biobank, and in recent years the data captured are extensive.

In summary, we have illustrated what can be achieved by a clinic delivering HIV care and treatment services to a rural population of around 600,000 people. Since its inception over ten years ago, the CDCI has undergone substantial changes; a number of improvements in patient care and outcomes have already been observed but the full implications of the changes remain to be seen. Future implementation of routine viral load monitoring and optimisation of patient tracking will also improve care and provide a more accurate picture of impact. These develop-ments and enhancedevelop-ments to data capture, allowing us to provide for example detailed data on OIs and laboratory abnormalities for which there are limited reports, together with the bio-bank, yield a rich resource which can be applied to address public health questions of direct relevance to inform further improvements in patient care.

Supporting information

S1 Fig. Flowchart of participants in the cohort.

(TIF)

Acknowledgments

The members of the KIULARCO Study Group are: Aschola Asantiel, Manuel Battegay, Adol-phina Chale, Diana Faini, Ingrid Felger, Gideon Francis, Hansjakob Furrer, Anna Gamell, Tracy R. Glass, Christoph Hatz, Speciosa Hwaya, Bryson Kasuga, Namvua Kimera, Yassin Kisunga, Thomas Klimkait, Emilio Letang, Antonia Luhombero, Lameck B. Luwanda, Herry Mapesi, Leticia Mbwile, Mengi Mkulila, Julius Mkumbo, Margareth Mkusa, Dorcas K. Mnzava, Germana Mossad, Dolores Mpundunga, Athuman Mtandanguo, Kim Mwamelo, Selerine Myeya, Sanula Nahota, Regina Ndaki, Agatha Ngulukila, Alex John Ntamatungiro, Leila Samson, George Sikalengo, Marcel Tanner, Fiona Vanobberghen, and Aneth Vedastus.

We thank the staff of the Chronic Disease Clinic of the St Francis Referral Hospital, Ifakara, Tanzania. We are grateful to all the participants of the Kilombero and Ulanga Antiretroviral Cohort (KIULARCO).

ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.

Author Contributions

Conceptualization: Fiona Vanobberghen, Tracy R. Glass.

Data curation: Emilio Letang, Anna Gamell, Dorcas K. Mnzava, Diana Faini, Lameck B.

Luwanda, Herry Mapesi, Kim Mwamelo, George Sikalengo.

Formal analysis: Fiona Vanobberghen. Funding acquisition: Marcel Tanner. Project administration: Emilio Letang.

Supervision: Emilio Letang, Marcel Tanner, Christoph Hatz, Hansjakob Furrer, Manuel

Bat-tegay, Tracy R. Glass.

Writing – original draft: Fiona Vanobberghen, Tracy R. Glass.

Writing – review & editing: Emilio Letang, Anna Gamell, Dorcas K. Mnzava, Diana Faini,

Lameck B. Luwanda, Herry Mapesi, Kim Mwamelo, George Sikalengo, Marcel Tanner, Christoph Hatz, Hansjakob Furrer, Manuel Battegay.

References

1. Joint United Nations Programme on HIV/AIDS (UNAIDS). Global report: UNAIDS report on the global AIDS epidemic 2013 [Internet]. 2013 [cited 16 Feb 2015].http://www.unaids.org/sites/default/files/en/ media/unaids/contentassets/documents/epidemiology/2013/gr2013/UNAIDS_Global_Report_2013_ en.pdf

2. Ministry of Health and Social Welfare; National AIDS Control Program. Implementation of HIV/AIDS care and treatment services in Tanzania—report number 3 [Internet]. May 2013 [cited 20 Nov 2014].

http://www.nacp.go.tz/site/publications/epidemiology-and-research-coordination

3. National Bureau of Statistics (NBS), Tanzania. 2002 and 2012 census population distribution as per 2012 population census administrative units [Internet]. [cited 6 Jul 2015].http://www.nbs.go.tz/ 4. Haraka F, Glass TR, Sikalengo G, Gamell A, Ntamatungiro A, Hatz C, et al. A bundle of services

increased ascertainment of tuberculosis among HIV-infected individuals enrolled in a HIV cohort in rural sub-Saharan Africa. PLoS ONE. 2015; 10: e0123275.https://doi.org/10.1371/journal.pone.0123275

PMID:25897491

5. Montlahuc C, Guiguet M, Abgrall S, Daneluzzi V, de Salvador F, Launay O, et al. Impact of late presen-tation on the risk of death among HIV-infected people in France (2003–2009). J Acquir Immune Defic Syndr 1999. 2013; 64: 197–203.https://doi.org/10.1097/QAI.0b013e31829cfbfaPMID:24047970 6. Waters L, Sabin CA. Late HIV presentation: epidemiology, clinical implications and management.

Expert Rev Anti Infect Ther. 2011; 9: 877–889.https://doi.org/10.1586/eri.11.106PMID:21973300 7. Delpierre C, Lauwers-Cances V, Pugliese P, Poizot-Martin I, Billaud E, Duvivier C, et al. Characteristics

trends, mortality and morbidity in persons newly diagnosed HIV positive during the last decade: the pro-file of new HIV diagnosed people. Eur J Public Health. 2008; 18: 345–347.https://doi.org/10.1093/ eurpub/ckm117PMID:18070812

8. Tanzania Ministry of Health and Social Welfare, National Tuberculosis and Leprosy Programme. Man-ual for the management of tuberculosis and leprosy: Sixth edition [Internet]. 2013 [cited 6 Jul 2015].

http://www.ntlp.go.tz/index.php?option=com_phocadownload&view=category&id=2:manuals&Itemid= 0

9. Letang E, Mu¨ller MC, Ntamatungiro AJ, Kimera N, Faini D, Furrer H, et al. Cryptococcal antigenemia in immunocompromised human immunodeficiency virus patients in rural Tanzania: A preventable cause of early mortality. Open Forum Infect Dis. 2015; 2.https://doi.org/10.1093/ofid/ofv046PMID:26213690 10. Gamell A, Glass TR, Luwanda LB, Mapesi H, Samson L, Mtoi T, et al. Implementation and Operational

Research: An Integrated and Comprehensive Service Delivery Model to Improve Pediatric and Maternal HIV Care in Rural Africa. J Acquir Immune Defic Syndr. 2016; 73: e67–e75.https://doi.org/10.1097/ QAI.0000000000001178PMID:27846070

11. World Health Organization. WHO international statistical classification of diseases and related health problems: 10th edition [Internet]. [cited 5 Feb 2014].http://apps.who.int/classifications/icd10/browse/ 2010/en

12. French M, Price P, Stone S. Immune restoration disease after antiretroviral therapy. Editorial review. AIDS. 2004; 18: 1615–1627. PMID:15280772

13. Meintjes G, Lawn SD, Scano F, Maartens G, French MA, Worodria W, et al. Tuberculosis-associated immune reconstitution inflammatory syndrome: case definitions for use in resource-limited settings. Lancet Infect Dis. 2008; 8: 516–523.https://doi.org/10.1016/S1473-3099(08)70184-1PMID:18652998 14. Haddow LJ, Colebunders R, Meintjes G, Lawn SD, Elliott JH, Manabe YC, et al. Cryptococcal immune

reconstitution inflammatory syndrome in HIV-1-infected individuals: proposed clinical case definitions. Lancet Infect Dis. 2010; 10: 791–802.https://doi.org/10.1016/S1473-3099(10)70170-5PMID:

21029993

15. Letang E, Naniche D, Bower M, Miro JM. Kaposi sarcoma-associated immune reconstitution inflamma-tory syndrome: in need of a specific case definition. Clin Infect Dis. 2012; 55: 157–158.https://doi.org/ 10.1093/cid/cis308PMID:22491336

16. World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity [Internet]. 2011 [cited 25 Aug 2015].http://www.who.int/vmnis/indicators/haemoglobin/en/ 17. Ruhl CE, Everhart JE. Upper limits of normal for alanine aminotransferase activity in the United States

population. Hepatol Baltim Md. 2012; 55: 447–454.https://doi.org/10.1002/hep.24725PMID:21987480 18. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to

esti-mate glomerular filtration rate. Ann Intern Med. 2009; 150: 604–612. PMID:19414839

19. Chi BH, Cantrell RA, Mwango A, Westfall AO, Mutale W, Limbada M, et al. An empirical approach to defining loss to follow-up among patients enrolled in antiretroviral treatment programs. Am J Epidemiol. 2010; 171: 924–931.https://doi.org/10.1093/aje/kwq008PMID:20219765

20. Pintilie M. Competing risks: A practical perspective. Chichester, UK: John Wiley & Sons, Ltd; 2006.

21. Franzeck FC, Letang E, Mwaigomole G, Jullu B, Glass TR, Nyogea D, et al. cART prescription trends in a prospective HIV cohort in rural Tanzania from 2007 to 2011. BMC Infect Dis. 2014; 14: 90.https://doi. org/10.1186/1471-2334-14-90PMID:24552395

22. Ramı´rez-Mena A, Glass TR, Winter A, Kimera N, Ntamatungiro A, Hatz C, et al. Prevalence and Out-comes of Hepatitis B Coinfection and Associated Liver Disease Among Antiretroviral Therapy-Naive Individuals in a Rural Tanzanian Human Immunodeficiency Virus Cohort. Open Forum Infect Dis. 2016; 3: ofw162.https://doi.org/10.1093/ofid/ofw162PMID:27704017

23. Joint United Nations Program on HIV/AIDS (UNAIDS). 90–90–90: An ambitious treatment target to help end the AIDS epidemic [Internet]. 2014 [cited 22 May 2015].http://www.unaids.org/sites/default/files/ media_asset/90-90-90_en_0.pdf

24. Mossdorf E, Stoeckle M, Vincenz A, Mwaigomole EG, Chiweka E, Kibatala P, et al. Impact of a national HIV voluntary counselling and testing (VCT) campaign on VCT in a rural hospital in Tanzania. Trop Med Int Health TM IH. 2010; 15: 567–573.https://doi.org/10.1111/j.1365-3156.2010.02490.xPMID:

20345555

25. Tanzania National AIDS Control Program. National guidelines for the management of HIV and AIDS (fourth edition) [Internet]. Apr 2012 [cited 1 Mar 2017].http://www.nacp.go.tz/site/download/ NationalGuidelineforManagementHIV.pdf

26. World Health Organization in partnership with UNICEF and UNAIDS. Global update on HIV treatment 2013: Results, impact and opportunities [Internet]. Jun 2013 [cited 30 Jul 2015].http://apps.who.int/iris/ bitstream/10665/85326/1/9789241505734_eng.pdf

27. The INSIGHT START Study Group. Initiation of antiretroviral therapy in early asymptomatic HIV infec-tion. N Engl J Med. 2015; 373: 795–807.https://doi.org/10.1056/NEJMoa1506816PMID:26192873 28. TEMPRANO ANRS 12136 Study Group, Danel C, Moh R, Gabillard D, Badje A, Le Carrou J, et al. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. N Engl J Med. 2015; 373: 808– 822.https://doi.org/10.1056/NEJMoa1507198PMID:26193126

29. Faini D, Kalinjuma A, Neborak J, King A, Mnzava D, Glass T, et al. Maximizing detection and improving outcomes of cryptococcosis in rural Tanzania [Internet]. Conference on Retroviruses and Opportunistic Infections; 2016 Feb 22; Boston, Massachusetts, USA.http://www.croiconference.org/sessions/ maximizing-detection-and-improving-outcomes-cryptococcosis-rural-tanzania-0

30. Faini D, Maokola W, Furrer H, Hatz C, Battegay M, Tanner M, et al. Burden of serious fungal infections in Tanzania. Mycoses. 2015; 58 Suppl 5: 70–79.https://doi.org/10.1111/myc.12390PMID:26449510 31. Mutyaba I, Phipps W, Krantz EM, Goldman JD, Nambooze S, Orem J, et al. A population-level

evalua-tion of the effect of antiretroviral therapy on cancer incidence in Kyadondo county, Uganda, 1999–2008. J Acquir Immune Defic Syndr. 2015; 69: 481–486.https://doi.org/10.1097/QAI.0000000000000620

PMID:25844696

32. Shiels MS, Engels EA. Evolving epidemiology of HIV-associated malignancies. Curr Opin HIV AIDS. 2017; 12: 6–11.https://doi.org/10.1097/COH.0000000000000327PMID:27749369

33. Amerson E, Woodruff CM, Forrestel A, Wenger M, McCalmont T, LeBoit P, et al. Accuracy of clinical suspicion and pathologic diagnosis of Kaposi sarcoma in East Africa. J Acquir Immune Defic Syndr. 2016; 71: 295–301.https://doi.org/10.1097/QAI.0000000000000862PMID:26452066

34. Wasserman S, Engel ME, Griesel R, Mendelson M. Burden of pneumocystis pneumonia in HIV-infected adults in sub-Saharan Africa: a systematic review and meta-analysis. BMC Infect Dis. 2016; 16: 482.

https://doi.org/10.1186/s12879-016-1809-3PMID:27612639

35. Mwita J, Mugusi F, Pallangyo K. Pneumocyctis pneumonia and pulmonary tuberculosis among HIV-infected patients at Muhimbili National Hospital, Tanzania. East Afr J Public Health. 2012; 9: 10–12. PMID:23120942

36. Jafari A, Khalili H, Dashti-Khavidaki S. Tenofovir-induced nephrotoxicity: incidence, mechanism, risk factors, prognosis and proposed agents for prevention. Eur J Clin Pharmacol. 2014; 70: 1029–1040.

37. Colebunders R, Kamya MR, Laurence J, Kambugu A, Byakwaga H, Mwebaze PS, et al. First-line anti-retroviral therapy in Africa—how evidence-base are our recommendations? AIDS Rev. 2005; 7: 148– 154. PMID:16302462

38. Parkes-Ratanshi R, Katende D, Levin J, Wakeham K, Heiner G, Kamali A, et al. Development of severe anemia and changes in hemoglobin in a cohort of HIV-infected Ugandan adults receiving zidovudine-, stavudine-, and tenofovir-containing antiretroviral regimens. J Int Assoc Provid AIDS Care. 2015; 14: 455–462.https://doi.org/10.1177/2325957414557264PMID:25425638

39. Zhou J, Jaquet A, Bissagnene E, Musick B, Wools-Kaloustian K, Maxwell N, et al. Short-term risk of anaemia following initiation of combination antiretroviral treatment in HIV-infected patients in countries in sub-Saharan Africa, Asia-Pacific, and central and South America. J Int AIDS Soc. 2012; 15: 5.https:// doi.org/10.1186/1758-2652-15-5PMID:22289654

40. Wandeler G, Gsponer T, Mulenga L, Garone D, Wood R, Maskew M, et al. Zidovudine impairs immuno-logical recovery on first-line antiretroviral therapy: collaborative analysis of cohort studies in Southern Africa. AIDS. 2013;https://doi.org/10.1097/QAD.0b013e328362d887PMID:23660577

41. Dong BJ, Zheng Y, Hughes MD, Frymoyer A, Verotta D, Lizak P, et al. Nevirapine pharmacokinetics and risk of rash and hepatitis among HIV-infected sub-Saharan African women. AIDS. 2012; 26: 833– 841.https://doi.org/10.1097/QAD.0b013e328351a521PMID:22301417

42. Jamisse L, Balkus J, Hitti J, Gloyd S, Manuel R, Osman N, et al. Antiretroviral-associated toxicity among HIV-1-seropositive pregnant women in Mozambique receiving nevirapine-based regimens. J Acquir Immune Defic Syndr. 2007; 44: 371–376.https://doi.org/10.1097/QAI.0b013e318032bbee

PMID:17259905

43. World Health Organization. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection - 2nd edition [Internet]. 2016.http://www.who.int/entity/hiv/pub/arv/arv-2016/ en/index.html

44. Tanzania National AIDS Control Program. National guidelines for the management of HIV and AIDS ( fifth edition) [Internet]. May 2015 [cited 2 Mar 2017].http://www.nacp.go.tz/site/download/ NationalGuidelineforManagementHIV.pdf

45. Bekolo CE, Webster J, Batenganya M, Sume GE, Kollo B. Trends in mortality and loss to follow-up in HIV care at the Nkongsamba Regional hospital, Cameroon. BMC Res Notes. 2013; 6: 512.https://doi. org/10.1186/1756-0500-6-512PMID:24308609

46. Lawn SD, Harries AD, Wood R. Strategies to reduce early morbidity and mortality in adults receiving antiretroviral therapy in resource-limited settings. Curr Opin HIV AIDS. 2010; 5: 18–26.https://doi.org/ 10.1097/COH.0b013e328333850fPMID:20046144

47. Brinkhof MWG, Pujades-Rodriguez M, Egger M. Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis. PloS One. 2009; 4: e5790.https://doi.org/10.1371/journal.pone.0005790PMID:19495419

48. Geng EH, Odeny TA, Lyamuya R, Nakiwogga-Muwanga A, Diero L, Bwana M, et al. Retention in care and patient-reported reasons for undocumented transfer or stopping care among HIV-infected patients on antiretroviral therapy in Eastern Africa: Application of a sampling-based approach. Clin Infect Dis Off Publ Infect Dis Soc Am. 2016; 62: 935–944.https://doi.org/10.1093/cid/civ1004PMID:26679625 49. Brinkhof MWG, Spycher BD, Yiannoutsos C, Weigel R, Wood R, Messou E, et al. Adjusting mortality for

loss to follow-up: analysis of five ART programmes in sub-Saharan Africa. PLoS ONE. 2010; 5: e14149.